Short‐term effects of benchmarking on the manufacturing practices and performance of SMEs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Facing increased competitive pressures due to globalisation and increased quality requirements from their customers, small and medium‐sized manufacturers must increase their productivity and their competitiveness in order to survive and prosper. One way of evaluating the attainment of this goal is to compare a firm's business practices and performance with those of a group of comparable firms, or with those of firms that are recognised for their excellence – that is, to “benchmark” the organisation. As management challenges have increased in complexity, benchmarking has become a strategic tool for organisations, both large and small, and for governments seeking to assist them. However, given a lack of empirical research, little is known as to the actual impacts of benchmarking. With this in mind, the present study sought to test a model of the relationship between benchmarking, the adoption of advanced manufacturing systems, and the performance of small to medium‐sized enterprises (SMEs). The model was tested with data from 102 Canadian manufacturing SMEs that have participated in a benchmarking exercise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it